Examples of systematic conservation planning approaches used for targeting nature recovery.
| Study . | Objective . | Targets . | Nature recovery actions . | Anticipated responses . | Costs . | Threats . | Prioritize actions . |
|---|---|---|---|---|---|---|---|
| Westphal and colleagues (2007) | Optimal landscape restoration for suite of bird species. | Maximize the summed probability of occurrence over all species and revegetation sites given a budget size | Revegetate to historical coverage | Species distribution models based on historical species records and vegetation coverage | Linear function of property value | None | Simulated annealing with custom objective function. |
| Thomson and colleagues (2009) | Spatial/temporal revegetation priorities to maximize habitat for birds (balanced solutions, no species doing poorly). | Rank sites by expected contribution to future biodiversity gain (habitat suitability) | Revegetation (recreate original state) | Occupancy models (habitat predictors) | None | None | Zonation |
| Strassburg and colleagues (2019) | Maximize ecosystem service benefits of forest restoration (biodiversity or carbon sequestration). | Scenarios with varied weightings applied to biodiversity and carbon targets, and cost constraints. | Proportion of forest historic extent to restore | Forecast extinction risk based on the species–area relationship, with potential distributions assuming restoration inferred using species distribution models; benefit of restoration assuming diminishing returns of adding more habitat to a unit. | Restoration uncertainty costs × planting costs + fencing costs. | None | Linear programming |
| Gilby and colleagues (2021 | Prioritize restoration to improve habitat quality. | Restore habitat matrix to increase total fish/harvestable fish abundance | Seagrass, oyster reef, mangrove restoration | Modelled relationship between fish abundance and extent of each habitat type | None | None | Bayesian belief network. |
| Shoo and colleagues (2021) | Schedule restoration to achieve maximum quality gain within budgetary constraints. | Scenario based, linked to perceived biodiversity value of being in each habitat state. | Restoring habitat across four discrete habitat states. | Scenario-based, assuming different biodiversity benefits of transitioning from one state to another and different timescales for habitat succession | Diminishing restoration costs through time, accounting for variations with site characteristics (e.g., accessibility). | None | Integer linear programming |
| Mu and colleagues (2022) | Restoration trade-offs of ecosystem services to maximize cobenefits. | Multiple scenarios for restoration area. | Restore farmland to forest or wetland (based on soil features or topography). | Four ecosystem services (carbon storage, soil retention, water yield, habitat quality); benefits calculated using a natural capital model. Habitat quality incorporates measures of threat and vulnerability to threats. | Opportunity (from cultivated land) and restoration (e.g., engineering) costs. | Via natural capital model | Marxan |
| Smith and colleagues (2022) | Identify a potential nature recovery network including core and recovery zones, with the latter managed to improve ecological conditions. | Expert set habitat-type targets. | Indirectly | Indirectly by including targets for habitat-types that could be restored. | Agricultural land quality | None | Marxan |
| Cattarino and colleagues (2015) | To prioritize the set of actions to address threats to freshwater fish species that achieves the conservation target at minimal cost. | Specific combination of actions necessary to remediate threats to species. | Multiple actions with potential to remediate threats to target species. | Species-specific responses to threats abatement, e.g., obtained from literature or plausible assumptions. | Land acquisition costs. | Considered as actions in this analytical framework. | Bespoke multiaction prioritization algorithm |
| Study . | Objective . | Targets . | Nature recovery actions . | Anticipated responses . | Costs . | Threats . | Prioritize actions . |
|---|---|---|---|---|---|---|---|
| Westphal and colleagues (2007) | Optimal landscape restoration for suite of bird species. | Maximize the summed probability of occurrence over all species and revegetation sites given a budget size | Revegetate to historical coverage | Species distribution models based on historical species records and vegetation coverage | Linear function of property value | None | Simulated annealing with custom objective function. |
| Thomson and colleagues (2009) | Spatial/temporal revegetation priorities to maximize habitat for birds (balanced solutions, no species doing poorly). | Rank sites by expected contribution to future biodiversity gain (habitat suitability) | Revegetation (recreate original state) | Occupancy models (habitat predictors) | None | None | Zonation |
| Strassburg and colleagues (2019) | Maximize ecosystem service benefits of forest restoration (biodiversity or carbon sequestration). | Scenarios with varied weightings applied to biodiversity and carbon targets, and cost constraints. | Proportion of forest historic extent to restore | Forecast extinction risk based on the species–area relationship, with potential distributions assuming restoration inferred using species distribution models; benefit of restoration assuming diminishing returns of adding more habitat to a unit. | Restoration uncertainty costs × planting costs + fencing costs. | None | Linear programming |
| Gilby and colleagues (2021 | Prioritize restoration to improve habitat quality. | Restore habitat matrix to increase total fish/harvestable fish abundance | Seagrass, oyster reef, mangrove restoration | Modelled relationship between fish abundance and extent of each habitat type | None | None | Bayesian belief network. |
| Shoo and colleagues (2021) | Schedule restoration to achieve maximum quality gain within budgetary constraints. | Scenario based, linked to perceived biodiversity value of being in each habitat state. | Restoring habitat across four discrete habitat states. | Scenario-based, assuming different biodiversity benefits of transitioning from one state to another and different timescales for habitat succession | Diminishing restoration costs through time, accounting for variations with site characteristics (e.g., accessibility). | None | Integer linear programming |
| Mu and colleagues (2022) | Restoration trade-offs of ecosystem services to maximize cobenefits. | Multiple scenarios for restoration area. | Restore farmland to forest or wetland (based on soil features or topography). | Four ecosystem services (carbon storage, soil retention, water yield, habitat quality); benefits calculated using a natural capital model. Habitat quality incorporates measures of threat and vulnerability to threats. | Opportunity (from cultivated land) and restoration (e.g., engineering) costs. | Via natural capital model | Marxan |
| Smith and colleagues (2022) | Identify a potential nature recovery network including core and recovery zones, with the latter managed to improve ecological conditions. | Expert set habitat-type targets. | Indirectly | Indirectly by including targets for habitat-types that could be restored. | Agricultural land quality | None | Marxan |
| Cattarino and colleagues (2015) | To prioritize the set of actions to address threats to freshwater fish species that achieves the conservation target at minimal cost. | Specific combination of actions necessary to remediate threats to species. | Multiple actions with potential to remediate threats to target species. | Species-specific responses to threats abatement, e.g., obtained from literature or plausible assumptions. | Land acquisition costs. | Considered as actions in this analytical framework. | Bespoke multiaction prioritization algorithm |
Examples of systematic conservation planning approaches used for targeting nature recovery.
| Study . | Objective . | Targets . | Nature recovery actions . | Anticipated responses . | Costs . | Threats . | Prioritize actions . |
|---|---|---|---|---|---|---|---|
| Westphal and colleagues (2007) | Optimal landscape restoration for suite of bird species. | Maximize the summed probability of occurrence over all species and revegetation sites given a budget size | Revegetate to historical coverage | Species distribution models based on historical species records and vegetation coverage | Linear function of property value | None | Simulated annealing with custom objective function. |
| Thomson and colleagues (2009) | Spatial/temporal revegetation priorities to maximize habitat for birds (balanced solutions, no species doing poorly). | Rank sites by expected contribution to future biodiversity gain (habitat suitability) | Revegetation (recreate original state) | Occupancy models (habitat predictors) | None | None | Zonation |
| Strassburg and colleagues (2019) | Maximize ecosystem service benefits of forest restoration (biodiversity or carbon sequestration). | Scenarios with varied weightings applied to biodiversity and carbon targets, and cost constraints. | Proportion of forest historic extent to restore | Forecast extinction risk based on the species–area relationship, with potential distributions assuming restoration inferred using species distribution models; benefit of restoration assuming diminishing returns of adding more habitat to a unit. | Restoration uncertainty costs × planting costs + fencing costs. | None | Linear programming |
| Gilby and colleagues (2021 | Prioritize restoration to improve habitat quality. | Restore habitat matrix to increase total fish/harvestable fish abundance | Seagrass, oyster reef, mangrove restoration | Modelled relationship between fish abundance and extent of each habitat type | None | None | Bayesian belief network. |
| Shoo and colleagues (2021) | Schedule restoration to achieve maximum quality gain within budgetary constraints. | Scenario based, linked to perceived biodiversity value of being in each habitat state. | Restoring habitat across four discrete habitat states. | Scenario-based, assuming different biodiversity benefits of transitioning from one state to another and different timescales for habitat succession | Diminishing restoration costs through time, accounting for variations with site characteristics (e.g., accessibility). | None | Integer linear programming |
| Mu and colleagues (2022) | Restoration trade-offs of ecosystem services to maximize cobenefits. | Multiple scenarios for restoration area. | Restore farmland to forest or wetland (based on soil features or topography). | Four ecosystem services (carbon storage, soil retention, water yield, habitat quality); benefits calculated using a natural capital model. Habitat quality incorporates measures of threat and vulnerability to threats. | Opportunity (from cultivated land) and restoration (e.g., engineering) costs. | Via natural capital model | Marxan |
| Smith and colleagues (2022) | Identify a potential nature recovery network including core and recovery zones, with the latter managed to improve ecological conditions. | Expert set habitat-type targets. | Indirectly | Indirectly by including targets for habitat-types that could be restored. | Agricultural land quality | None | Marxan |
| Cattarino and colleagues (2015) | To prioritize the set of actions to address threats to freshwater fish species that achieves the conservation target at minimal cost. | Specific combination of actions necessary to remediate threats to species. | Multiple actions with potential to remediate threats to target species. | Species-specific responses to threats abatement, e.g., obtained from literature or plausible assumptions. | Land acquisition costs. | Considered as actions in this analytical framework. | Bespoke multiaction prioritization algorithm |
| Study . | Objective . | Targets . | Nature recovery actions . | Anticipated responses . | Costs . | Threats . | Prioritize actions . |
|---|---|---|---|---|---|---|---|
| Westphal and colleagues (2007) | Optimal landscape restoration for suite of bird species. | Maximize the summed probability of occurrence over all species and revegetation sites given a budget size | Revegetate to historical coverage | Species distribution models based on historical species records and vegetation coverage | Linear function of property value | None | Simulated annealing with custom objective function. |
| Thomson and colleagues (2009) | Spatial/temporal revegetation priorities to maximize habitat for birds (balanced solutions, no species doing poorly). | Rank sites by expected contribution to future biodiversity gain (habitat suitability) | Revegetation (recreate original state) | Occupancy models (habitat predictors) | None | None | Zonation |
| Strassburg and colleagues (2019) | Maximize ecosystem service benefits of forest restoration (biodiversity or carbon sequestration). | Scenarios with varied weightings applied to biodiversity and carbon targets, and cost constraints. | Proportion of forest historic extent to restore | Forecast extinction risk based on the species–area relationship, with potential distributions assuming restoration inferred using species distribution models; benefit of restoration assuming diminishing returns of adding more habitat to a unit. | Restoration uncertainty costs × planting costs + fencing costs. | None | Linear programming |
| Gilby and colleagues (2021 | Prioritize restoration to improve habitat quality. | Restore habitat matrix to increase total fish/harvestable fish abundance | Seagrass, oyster reef, mangrove restoration | Modelled relationship between fish abundance and extent of each habitat type | None | None | Bayesian belief network. |
| Shoo and colleagues (2021) | Schedule restoration to achieve maximum quality gain within budgetary constraints. | Scenario based, linked to perceived biodiversity value of being in each habitat state. | Restoring habitat across four discrete habitat states. | Scenario-based, assuming different biodiversity benefits of transitioning from one state to another and different timescales for habitat succession | Diminishing restoration costs through time, accounting for variations with site characteristics (e.g., accessibility). | None | Integer linear programming |
| Mu and colleagues (2022) | Restoration trade-offs of ecosystem services to maximize cobenefits. | Multiple scenarios for restoration area. | Restore farmland to forest or wetland (based on soil features or topography). | Four ecosystem services (carbon storage, soil retention, water yield, habitat quality); benefits calculated using a natural capital model. Habitat quality incorporates measures of threat and vulnerability to threats. | Opportunity (from cultivated land) and restoration (e.g., engineering) costs. | Via natural capital model | Marxan |
| Smith and colleagues (2022) | Identify a potential nature recovery network including core and recovery zones, with the latter managed to improve ecological conditions. | Expert set habitat-type targets. | Indirectly | Indirectly by including targets for habitat-types that could be restored. | Agricultural land quality | None | Marxan |
| Cattarino and colleagues (2015) | To prioritize the set of actions to address threats to freshwater fish species that achieves the conservation target at minimal cost. | Specific combination of actions necessary to remediate threats to species. | Multiple actions with potential to remediate threats to target species. | Species-specific responses to threats abatement, e.g., obtained from literature or plausible assumptions. | Land acquisition costs. | Considered as actions in this analytical framework. | Bespoke multiaction prioritization algorithm |
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